Let’s become better data scientists by avoiding common pitfalls. Follow these basic principles to make your machine learning projects more impactful.
Author: Ingo Mierswa
Resilience is the new accuracy in data science projects. Here’s why your “best” model might not be the best at all…
What’s coming down the pipe for AI and machine learning in 2020 and beyond?
Data Science: Concepts and Practice (Second Edition) by Vijau Kotu and Bala Deshpande is now available. Order your copy today.
What makes data prep so difficult and tedious? Ingo shares his thoughts on this and how RapidMiner addresses this issue with a new data prep approach.
Machine learning and data science have become an intrinsic part of business. Learn how to avoid common data science mistakes that can ruin your business.
Read through a demonstration of Turbo Prep and Auto Model by Ingo Mierswa to see how RapidMiner makes data prep and machine learning fun, fast, and simple.
One of the most frequent questions I get asked is: “Ingo, I am from Industry X and my data looks like Y and my colleague recommended to use model Z – what is your opinion on what model to use?” In this blog post, I explain a well-proven framework for model selection.
In Part 4 of this series we discuss multi-objective feature selection, which can be used for unsupervised learning & to identify best spaces for clusters.
Multi-objective optimization is great for feature selection because we can find all potentially good solutions without defining a trade-off factor.